What does the future look like for AI?
Michael Running Wolf (Northern Cheyenne) was raised in a rural village in Montana with intermittent water and electricity. He attended the March 2019 Indigenous Protocol and Artificial Intelligence workshops in Hawai’i. Here he explores the future of AI.
The future is the continuing proliferation and accessibility of Machine Learning (ML). Though the fundamental math and technology has not changed, the access and relative ease to create advanced AI systems has. A mere generation ago custom built supercomputers, and millions of dollars of investment, was the minimal entry fee to use ML. Now, in addition to the advent of the Open Source Software (OSS) movement, ML is consumer grade. One could build a reasonable ML computer with top of the line software tooling for less than $1,000! Even that is not strictly necessary, all you need is a web browser to access cloud computing. One could, for a fee, deploy a supercomputer cluster within minutes. For Indigenous nations, this access is at once an opportunity and risk.
The AI tooling to suppress Native activists, protecting sacred lands, is easily purchased by antagonistic special interests. One not need be a well financed national state, small agencies can easily license facial recognition software to monitor ‘radical environmentalists’ protecting their sacred lands from exploitation. Advanced facial recognition turns any phone into a potential spy while social media photo platforms are susceptible to analysis. Though our privacy is at risk, the benefits outway the risk.
Every internet user is a few minutes away from deploying their very own ML infrastructure and a wealth of research. TensorFlow, the most popular ML framework for instance, is freely available and gives community researchers access to millions of dollars of research development investment. We are limited only by time and skill.
Initially, a tribe’s community researchers could collect the decades of anthropological and linguistic research collected in mountainous digital archives. A researcher can expect to barely scratch the surface of this knowledge if they diligently read every word. However, with advanced text analysis one can quickly mine the knowledge to rediscover lost insights into their own tribe. These insights can then form the building blocks for advanced cultural and linguistic revitalization tooling.
For example, one could textmine the Hawaiian news archive, the Papakilo Database, and build a statistical language corpus. With this corpus one could train recognition and generative ML systems, i.e. a way of validating proper Hawaiian grammar while also creating a mechanism to generate new sentences. With these tools in hand one can create a Hawaiian chatbot! With phonemes and audio recognition you are inches away from creating an Indigenous Voice AI similar to Apple Siri or Google Assistant. Imagine Virtual Reality worlds populated by intelligent Hawaiian language speakers wanting nothing more than to teach you a new language. Everyone needs an infinitely patient Indigenous personal language teacher.
Despite the risk, ML offers opportunity for Indigenous communities. In fact we have little choice, Machine Learning will be leveraged against us or by us.
Michael Running Wolf (Northern Cheyenne) was raised in a rural village in Montana with intermittent water and electricity. Naturally, he now has a Masters of Science in Computer Science. Though he is a published poet, he is a computer nerd at heart. His lifelong goal is to pursue endangered indigenous language revitalization using Augmented Reality and Virtual Reality (AR/VR) technology. He was raised with a grandmother who only spoke his tribal language, Cheyenne, which like many other indigenous languages, is near extinction. By leveraging his advanced degree and technical skills, Running Wolf hopes to strengthen the ecology of thought represented by indigenous languages through immersive technology.
The Indigenous Protocols and Artificial Intelligence (IP-AI) workshops are founded by Old Ways, New, and the Initiative for Indigenous Futures. This work is funded by the Canadian Institute for Advanced Research (CIFAR), Old Ways, New, the Social Sciences and Humanities Research Council (SSHRC) and the Concordia University Research Chair in Computational Media and the Indigenous Future Imaginary.